Regression Models as a Tool in Medical Research

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the results is often challenging for beginners. Regression Models as a Tool in Medical Research presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data.The focus is always on adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.

After reviewing popular models and basic methods, the book focuses on advanced topics and techniques. It considers the comparison of regression coefficients,the selection of covariates, modelling of non-linear and non-additive effects, the analysis of clustered and longitudinal data, and highlights the impact of selection mechanisms, measurement error, and incomplete covariate data. The text then covers the use of regression models to construct risk scores and predictors. It also gives an overview on more specific regression models and their applications as well as alternatives to regression modeling. The mathematical details underlying the estimation and inference techniques are provided in the appendices.

Features:

Helps readers improve their understanding of the role of regression models in the medical field

Illustrates each technique with a concrete example, enabling readers to better appreciate the properties and theory of the methods

Uses Stata to illustrate the practical use

Discusses how and when regression models can fail

Describes the basic principles behind statistical computations, with more mathematical details given in the appendices

Offers the data sets,solutions to all exercises, and a short introduction to Stata on this page here